Small RNAseq: Differential Expression Analysis
Environment Setup
salloc -N 1 --exclusive -p amd -t 8:00:00
conda env create -f conda-env.yml
conda activate smallrnaDownloading datasets
Raw data
Raw data was downloaded from the sequencing facility using the secure
link, with wget command. The downloaded files were checked
for md5sum and compared against list of files expected as per the input
samples provided.
wget https://oc1.rnet.missouri.edu/xyxz
# link masked
# GEO link will be included later
# merge files of same samples (technical replicates)
paste <(ls *_L001_R1_001.fastq.gz) <(ls *_L002_R1_001.fastq.gz) | \
sed 's/\t/ /g' |\
awk '{print "cat",$1,$2" > "$1}' |\
sed 's/_L001_R1_001.fastq.gz/.fq.gz/2' > concatenate.sh
chmod +x concatenate.sh
sh concatenate.shGenome/annotation
Additional files required for the analyses were downloaded from GenCode. The downloaded files are as follows:
wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M30/GRCm39.primary_assembly.genome.fa.gz
wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M30/gencode.vM30.annotation.gff3.gz
wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M30/gencode.vM30.annotation.gtf.gz
gunzip GRCm39.primary_assembly.genome.fa.gz
gunzip gencode.vM30.annotation.gff3.gz
gunzip gencode.vM30.annotation.gtf.gzFastQC (before processing)
for fq in *.fq.gz; do
fastqc --threads $SLURM_JOB_CPUS_PER_NODE $fq;
done
mkdir -p fastqc_pre
mv *.zip *.html fastqc_pre/Mapping
To index the genome, following command was run (in an interactive session).
fastaGenome="GRCm39.genome.fa"
gtf="gencode.vM30.annotation.gtf"
STAR --runThreadN $SLURM_JOB_CPUS_PER_NODE \
--runMode genomeGenerate \
--genomeDir $(pwd) \
--genomeFastaFiles $fastaGenome \
--sjdbGTFfile $gtf \
--sjdbOverhang 1Each fastq file was mapped to the indexed genome as
using runSTAR_map.sh script shown below:
#!/bin/bash
read1=$1
out=$(basename ${read1%%.*})
STARgenomeDir=$(pwd)
# illumina adapter
adapterseq="AGATCGGAAGAGC"
STAR \
--genomeDir ${STARgenomeDir} \
--readFilesIn ${read1} \
--outSAMunmapped Within \
--readFilesCommand zcat \
--outSAMtype BAM SortedByCoordinate \
--quantMode GeneCounts \
--outFilterMultimapNmax 20 \
--clip3pAdapterSeq ${adapterseq} \
--clip3pAdapterMMp 0.1 \
--outFilterMismatchNoverLmax 0.03 \
--outFilterScoreMinOverLread 0 \
--outFilterMatchNminOverLread 0 \
--outFilterMatchNmin 16 \
--outFileNamePrefix ${out} \
--alignSJDBoverhangMin 1000 \
--alignIntronMax 1 \
--runThreadN ${SLURM_JOB_CPUS_PER_NODE} \
--genomeLoad LoadAndKeep \
--limitBAMsortRAM 30000000000 \
--outSAMheaderHD "@HD VN:1.4 SO:coordinate"Mapping was run with a simple loop:
for fq in *.fq.gz; do
runSTAR_map.sh $fq;
doneCounting Stats
Counts generated by STAR with option
--quantMode GeneCounts were parsed to generate summary
stats as well as to extract annotated small RNA feature counts.
mkdir -p counts_files
# copy counts for each sample
cp *ReadsPerGene.out.tab counts_files/
cd counts_files
# merge counts
join_files.sh *ReadsPerGene.out.tab |\
sed 's/ReadsPerGene.out.tab//g' |\
grep -v "^N_" > counts_star.tsv
# merge stats
join_files.sh *ReadsPerGene.out.tab |\
sed 's/ReadsPerGene.out.tab//g' |\
head -n 1 > summary_star.tsv
join_files.sh *ReadsPerGene.out.tab |\
sed 's/ReadsPerGene.out.tab//g' |\
grep "^N_" >> summary_star.tsv
# parse GTF to extact gene.id and its biotype:
gtf=gencode.vM30.annotation.gtf
awk 'BEGIN{OFS=FS="\t"} $3=="gene" {split($9,a,";"); print a[1],a[2]}' ${gtf} |\
awk '{print $4"\t"$2}' |\
sed 's/"//g' > GeneType_GeneID.tsv
cut -f 1 GeneType_GeneID.tsv | sort |uniq > features.txtThe information for biotype as provided by the gencodegenes
were used for categorizing biotype.
The smallRNA group consists of following
biotype:
miRNA
misc_RNA
scRNA
snRNA
snoRNA
sRNA
scaRNA
The full table is as follows:
library(knitr)
setwd("/work/LAS/geetu-lab/arnstrm/mouse.trophoblast.smallRNAseq")
file1="assets/GeneType_Group.tsv"
info <-
read.csv(
file1,
header = TRUE,
sep = "\t",
stringsAsFactors = TRUE
)
kable(info, caption = "Table 1: biotype and its groupings")| biotype | group |
|---|---|
| protein_coding | coding_genes |
| pseudogene | pseudogenes |
| TR_C_gene | Ig_genes |
| TR_D_gene | Ig_genes |
| TR_J_gene | Ig_genes |
| TR_V_gene | Ig_genes |
| IG_C_gene | Ig_genes |
| IG_D_gene | Ig_genes |
| IG_J_gene | Ig_genes |
| IG_LV_gene | Ig_genes |
| IG_V_gene | Ig_genes |
| TR_J_pseudogene | pseudogenes |
| TR_V_pseudogene | pseudogenes |
| IG_C_pseudogene | pseudogenes |
| IG_D_pseudogene | pseudogenes |
| IG_pseudogene | pseudogenes |
| IG_V_pseudogene | pseudogenes |
| lncRNA | long_non_conding_RNA |
| miRNA | non_conding_RNA |
| misc_RNA | non_conding_RNA |
| ribozyme | non_conding_RNA |
| rRNA | non_conding_RNA |
| scaRNA | non_conding_RNA |
| scRNA | non_conding_RNA |
| snoRNA | non_conding_RNA |
| snRNA | non_conding_RNA |
| sRNA | non_conding_RNA |
| Mt_rRNA | non_conding_RNA |
| Mt_tRNA | non_conding_RNA |
| processed_pseudogene | pseudogenes |
| unprocessed_pseudogene | pseudogenes |
| translated_unprocessed_pseudogene | pseudogenes |
| transcribed_processed_pseudogene | pseudogenes |
| transcribed_unitary_pseudogene | pseudogenes |
| transcribed_unprocessed_pseudogene | pseudogenes |
| unitary_pseudogene | pseudogenes |
| TEC | unconfirmed_genes |
A samples table (samples.tsv) categorizing samples to
its condition were also generated:
file2="assets/samples.tsv"
samples <-
read.csv(
file2,
header = TRUE,
sep = "\t",
stringsAsFactors = TRUE
)
kable(samples, caption = "Table 2: Samples in the study")| Sample | Group |
|---|---|
| Dif_D6_1_S4 | Diff |
| Dif_D6_2_S3 | Diff |
| Dif_D6_3_S2 | Diff |
| Dif_D6_4_S1 | Diff |
| Undif_D2_1_S8 | Undf |
| Undif_D2_2_S7 | Undf |
| Undif_D2_3_S6 | Undf |
| Undif_D2_4_S5 | Undf |
This information was then merged withe counts table to generate QC plots:
awk 'BEGIN{OFS=FS="\t"}FNR==NR{a[$1]=$2;next}{ print $2,$1,a[$1]}' \
GeneType_Group.tsv GeneType_GeneID.tsv > GeneID_GeneType_Group.tsv
awk 'BEGIN{OFS=FS="\t"}FNR==NR{a[$1]=$2"\t"$3;next}{print $1,a[$1],$0}' \
GeneID_GeneType_Group.tsv counts_star.tsv |\
cut -f 1-3,5- > processed_counts_star.tsvPlotting the mapping summary and count statistics for various biotypes:
library(scales)
library(tidyverse)
library(plotly)setwd("/work/LAS/geetu-lab/arnstrm/mouse.trophoblast.smallRNAseq")
file1="assets/processed_counts_star.tsv"
file2="assets/summary_stats_star.tsv"
counts <-
read.csv(
file1,
sep = "\t",
stringsAsFactors = TRUE
)
subread <-
read.csv(
file2,
sep = "\t",
stringsAsFactors = TRUE
)
# convert long format
counts.long <- gather(counts, Sample, Count, Dif_D6_1_S4:Undif_D2_4_S5, factor_key=TRUE)
subread.long <- gather(subread, Sample, Count, Dif_D6_1_S4:Undif_D2_4_S5, factor_key=TRUE)
# organize
counts.long$Group <-
factor(
counts.long$Group,
levels = c(
"coding_genes",
"non_conding_RNA",
"long_non_conding_RNA",
"pseudogenes",
"unconfirmed_genes",
"Ig_genes"
)
)
subread.long$Assignments <-
factor(
subread.long$Assignments,
levels = c(
"N_input",
"N_unmapped",
"N_multimapping",
"N_unique",
"N_ambiguous",
"N_noFeature"
)
)ggplot(subread.long, aes(x = Assignments, y = Count, fill = Assignments)) +
geom_bar(stat = 'identity') +
labs(x = "Subread assingments", y = "reads") + theme_minimal() +
scale_y_continuous(labels = label_comma()) +
theme(
axis.text.x = element_text(
angle = 45,
vjust = 1,
hjust = 1,
size = 12
),
strip.text = element_text(
face = "bold",
color = "gray35",
hjust = 0,
size = 10
),
strip.background = element_rect(fill = "white", linetype = "blank"),
legend.position = "none"
) +
facet_wrap("Sample", scales = "free_y", ncol = 4)
Figure 1: STAR read mapping and feature assignment. Here,
N_input is total input reads, N_unmapped is
reads that were either too short to map after adapter removal or had
higher mismatch rate to place reliably on the genome,
N_multimapping is reads mapped to multiple loci,
N_unique is reads mapped to unique loci. A subset of
N_unique reads that were unable to clearly assign to a
feature or assign any feature at all are grouped as
N_ambigious or N_noFeature, respectively
g <- ggplot(counts.long, aes(x = Group, y = Count, fill = Group)) +
geom_bar(stat = 'sum') +
labs(x = "biotype", y = "read counts") + theme_minimal() +
scale_y_continuous(labels = label_comma()) +
theme(
axis.text.x = element_text(
angle = 45,
vjust = 1,
hjust = 1,
size = 12
),
strip.text = element_text(
face = "bold",
color = "gray35",
hjust = 0,
size = 10
),
strip.background = element_rect(fill = "white", linetype = "blank"),
legend.position = "none"
) +
facet_wrap("Sample", scales = "free_y", ncol = 4)
#ggplotly(g)
gFigure 2: Features with read counts
counts.nc <- filter(counts.long, Group %in% "non_conding_RNA" )
counts.nc$GeneType <-
factor(
counts.nc$GeneType,
levels = c(
"miRNA",
"misc_RNA",
"snoRNA",
"snRNA",
"sRNA",
"scRNA",
"scaRNA",
"Mt_tRNA",
"Mt_rRNA",
"rRNA",
"ribozyme"
)
)
g <- ggplot(counts.nc, aes(x = GeneType, y = Count, fill = GeneType)) +
geom_bar(stat = 'sum') +
labs(x = "biotype", y = "read counts") + theme_minimal() +
scale_y_continuous(labels = label_comma()) +
theme(
axis.text.x = element_text(
angle = 45,
vjust = 1,
hjust = 1,
size = 12
),
strip.text = element_text(
face = "bold",
color = "gray35",
hjust = 0,
size = 10
),
strip.background = element_rect(fill = "white", linetype = "blank"),
legend.position = "none"
) +
facet_wrap("Sample", scales = "free_y", ncol = 4)
#ggplotly(g)
gFigure 3: non-coding biotype read counts
subset the counts file to select only smallRNA genes
snrna <- c('miRNA',
'misc_RNA',
'scRNA',
'snRNA',
'snoRNA',
'sRNA',
'scaRNA')
cts <- filter(counts, GeneType %in% snrna) %>%
select(Geneid, Dif_D6_1_S4:Undif_D2_4_S5)
write_delim(cts, file = "assets/noncoding_counts_star.tsv", delim = "\t")This noncoding_counts_star.tsv and
samples.tsv file will be used for DESeq2
analyses.
DESeq2
For the next steps, we used DESeq2 for performing the DE
analyses. Results were visualized as volcano plots and tables were
exported to excel.
Load packages
setwd("/work/LAS/geetu-lab/arnstrm/mouse.trophoblast.smallRNAseq")
library(DESeq2)
library(RColorBrewer)
library(pheatmap)
library(genefilter)
library(ggrepel)
library(biomaRt)
library(reshape2)
library(PupillometryR)
library(ComplexUpset)Import counts and sample metadata
The counts data and its associated metadata
(coldata) are imported for analyses.
counts = 'assets/noncoding_counts_star.tsv'
groupFile = 'assets/samples.tsv'
coldata <-
read.csv(
groupFile,
row.names = 1,
sep = "\t",
stringsAsFactors = TRUE
)
cts <- as.matrix(read.csv(counts, sep = "\t", row.names = "Geneid"))
Reorder columns of cts according to coldata
rows. Check if samples in both files match.
colnames(cts)
#> [1] "Dif_D6_1_S4" "Dif_D6_2_S3" "Dif_D6_3_S2" "Dif_D6_4_S1"
#> [5] "Undif_D2_1_S8" "Undif_D2_2_S7" "Undif_D2_3_S6" "Undif_D2_4_S5"
all(rownames(coldata) %in% colnames(cts))
#> [1] TRUE
cts <- cts[, rownames(coldata)]Normalize
The batch corrected read counts are then used for running DESeq2 analyses
dds <- DESeqDataSetFromMatrix(countData = cts,
colData = coldata,
design = ~ Group)
vsd <- vst(dds, blind = FALSE, nsub =500)
keep <- rowSums(counts(dds)) >= 5
dds <- dds[keep, ]
dds <- DESeq(dds)
dds
#> class: DESeqDataSet
#> dim: 1266 8
#> metadata(1): version
#> assays(4): counts mu H cooks
#> rownames(1266): ENSMUSG00000119106.1 ENSMUSG00000119589.1 ...
#> ENSMUSG00000065444.3 ENSMUSG00000077869.3
#> rowData names(22): baseMean baseVar ... deviance maxCooks
#> colnames(8): Dif_D6_1_S4 Dif_D6_2_S3 ... Undif_D2_3_S6 Undif_D2_4_S5
#> colData names(2): Group sizeFactorvst <- assay(vst(dds, blind = FALSE, nsub = 500))
vsd <- vst(dds, blind = FALSE, nsub = 500)
pcaData <-
plotPCA(vsd,
intgroup = "Group",
returnData = TRUE)
percentVar <- round(100 * attr(pcaData, "percentVar"))PCA plot for QC
PCA plot for the dataset that includes all libraries.
rv <- rowVars(assay(vsd))
select <-
order(rv, decreasing = TRUE)[seq_len(min(500, length(rv)))]
pca <- prcomp(t(assay(vsd)[select, ]))
percentVar <- pca$sdev ^ 2 / sum(pca$sdev ^ 2)
intgroup = "Group"
intgroup.df <- as.data.frame(colData(vsd)[, intgroup, drop = FALSE])
group <- if (length(intgroup) == 1) {
factor(apply(intgroup.df, 1, paste, collapse = " : "))
}
d <- data.frame(
PC1 = pca$x[, 1],
PC2 = pca$x[, 2],
intgroup.df,
name = colnames(vsd)
)plot PCA for components 1 and 2
#nudge <- position_nudge(y = 0.5)
g <- ggplot(d, aes(PC1, PC2, color = Group)) +
scale_shape_manual(values = 1:8) +
theme_bw() +
theme(legend.title = element_blank()) +
geom_point(size = 2, stroke = 2) +
geom_text_repel(aes(label = name)) +
xlab(paste("PC1", round(percentVar[1] * 100, 2), "% variance")) +
ylab(paste("PC2", round(percentVar[2] * 100, 2), "% variance"))
gFigure 4: PCA plot for the first 2 principal components
#gSample distance for QC
sampleDists <- dist(t(assay(vsd)))
sampleDistMatrix <- as.matrix( sampleDists )
rownames(sampleDistMatrix) <- colnames(vsd)
colnames(sampleDistMatrix) <- NULL
colors <- colorRampPalette( rev(brewer.pal(9, "Blues")) )(255)
pheatmap(sampleDistMatrix,
clustering_distance_rows = sampleDists,
clustering_distance_cols = sampleDists,
col = colors)Figure 5: Euclidean distance between samples
Set contrasts and find DE genes
resultsNames(dds)
#> [1] "Intercept" "Group_Undf_vs_Diff"
res.UndfvsDiff <- results(dds, contrast = c("Group", "Undf", "Diff"))
table(res.UndfvsDiff$padj < 0.05)
#>
#> FALSE TRUE
#> 579 294
res.UndfvsDiff <- res.UndfvsDiff[order(res.UndfvsDiff$padj),]
res.UndfvsDiffdata <-
merge(
as.data.frame(res.UndfvsDiff),
as.data.frame(counts(dds, normalized = TRUE)),
by = "row.names",
sort = FALSE
)Creating gene lists
The gene lists have Ensembl gene-ID-version. We need
mirbase_id, gene_biotype and
external_gene_name attached to make the results
interpretable. So we will download metadata from ensembl using
biomaRt package.
ensembl = useMart("ENSEMBL_MART_ENSEMBL")
ensembl = useDataset("mmusculus_gene_ensembl", mart = ensembl)
filterType <- "ensembl_gene_id_version"
filterValues <- rownames(cts)
attributeNames <- c('ensembl_gene_id_version',
'gene_biotype',
'mirbase_id',
'external_gene_name')
annot <- getBM(
attributes = attributeNames,
filters = filterType,
values = filterValues,
mart = ensembl
)
isDup <- duplicated(annot$ensembl_gene_id)
dup <- annot$ensembl_gene_id[isDup]
annot <- annot[!annot$ensembl_gene_id %in% dup, ] #this object will be saved and used laterWrite result tables
Here, we will attach the mirbase_id,
gene_biotype and external_gene_name downloaded
in the previous section to the results table. We will also filter the
the table to write:
- DE list that have
padjless than or equal to 0.05 - DE list that have
padjless than or equal to 0.05, andlog2FoldChangegreater than or equal to fold change of 1.5 - DE list that have
padjless than or equal to 0.05, andlog2FoldChangeless than or equal to fold change of -1.5 - Full list of DE table without any filtering.
names(res.UndfvsDiffdata)[1] <- "ensembl_gene_id_version"
res.UndfvsDiffdata <-
merge(x = res.UndfvsDiffdata,
y = annot,
by = "ensembl_gene_id_version",
all.x = TRUE)
res.UndfvsDiffdata <-
res.UndfvsDiffdata[, c(1, (ncol(res.UndfvsDiffdata) - 2):ncol(res.UndfvsDiffdata), 2:(ncol(res.UndfvsDiffdata) -
3))]
res.UndfvsDiffSig <- res.UndfvsDiffdata %>%
filter(padj <= 0.05)
fc = 1.5
log2fc = log(fc, base = 2)
neg.log2fc = log2fc * -1
res.UndfvsDiffSig.up <- res.UndfvsDiffdata %>%
filter(padj <= 0.05 & log2FoldChange >= log2fc)
res.UndfvsDiffSig.dw <- res.UndfvsDiffdata %>%
filter(padj <= 0.05 & log2FoldChange <= neg.log2fc)
write_delim(res.UndfvsDiffdata, file = "DESeq2_results_UndfvsDiff_full-table.tsv", delim = "\t")
write_delim(res.UndfvsDiffSig, file = "DESeq2_results_UndfvsDiff_adj.p-le-0.05.tsv", delim = "\t")
write_delim(res.UndfvsDiffSig.up, file = "DESeq2_results_UndfvsDiff_adj.p-le-0.05_fc-ge-1.5.tsv", delim = "\t")
write_delim(res.UndfvsDiffSig.dw, file = "DESeq2_results_UndfvsDiff_adj.p-le-0.05_fc-le-neg1.5.tsv", delim = "\t")Highly expressed genes for conditions
exp <- res.UndfvsDiffdata[, c(1:4, 11:ncol(res.UndfvsDiffdata))]
exp$external_gene_name <-
ifelse(exp$external_gene_name == "",
exp$ensembl_gene_id_version,
exp$external_gene_name)
exp$gene <- paste0(exp$external_gene_name, "(", exp$gene_biotype, ")")
renamed.exp <- exp[, c(13, 1:12)]
renamed.exp.long <-
melt(
renamed.exp,
id.vars = c(
'gene',
'ensembl_gene_id_version',
'gene_biotype',
'mirbase_id',
'external_gene_name'
)
)
colnames(renamed.exp.long) <-
c('gene',
'ensembl_gene_id_version',
'gene_biotype',
'mirbase_id',
'external_gene_name',
'replicates',
'norm.expression'
)
renamed.exp.long$condition <- "NA"
renamed.exp.long$condition[which(str_detect(renamed.exp.long$replicates, "Dif_D6"))] <-
"Diff"
renamed.exp.long$condition[which(str_detect(renamed.exp.long$replicates, "Undif_D2"))] <-
"Undiff"
renamed.exp.long <-
renamed.exp.long %>% filter(norm.expression > 0)
renamed.exp.long <- na.omit(renamed.exp.long)
# SOURCE: https://ourcodingclub.github.io/tutorials/dataviz-beautification/
theme_niwot <- function() {
theme_bw() +
theme(
axis.text = element_text(size = 10),
axis.title = element_text(size = 12),
axis.line.x = element_line(color = "black"),
axis.line.y = element_line(color = "black"),
panel.border = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.major.y = element_blank(),
plot.margin = unit(c(1, 1, 1, 1), units = , "cm"),
plot.title = element_text(
size = 8,
vjust = 1,
hjust = 0
),
legend.text = element_text(size = 8),
legend.title = element_blank(),
legend.position = c(0.95, 0.15),
legend.key = element_blank(),
legend.background = element_rect(
color = "black",
fill = "transparent",
size = 2,
linetype = "blank"
)
)
}Normalized expression plots
A <- ggplot(data = renamed.exp.long,
aes(x = replicates, y = norm.expression, fill = condition)) +
geom_flat_violin(position = position_nudge(x = 0.2, y = 0),
alpha = 0.8,
trim = FALSE) +
geom_point(
aes(y = norm.expression, color = condition),
position = position_jitter(width = 0.15),
size = 1,
alpha = 0.5
) +
geom_boxplot(width = 0.2,
outlier.shape = NA,
alpha = 0.8) + stat_summary(
fun = mean,
geom = "point",
shape = 23,
size = 2
) +
labs(y = "\nNormalized Expression", x = NULL) +
guides(fill = "none", color = "none") +
scale_y_log10(label = comma) +
theme_niwot() + theme(axis.text.x = element_text(
angle = 45,
vjust = 1,
hjust = 1
))B <- ggplot(data = renamed.exp.long,
aes(x = condition, y = norm.expression, fill = condition)) +
geom_flat_violin(position = position_nudge(x = 0.2, y = 0),
alpha = 0.8,
trim = FALSE) +
geom_point(
aes(y = norm.expression, color = condition),
position = position_jitter(width = 0.15),
size = 1,
alpha = 0.5
) +
geom_boxplot(width = 0.2,
outlier.shape = NA,
alpha = 0.8) + stat_summary(
fun = mean,
geom = "point",
shape = 23,
size = 2
) +
labs(y = "\nNormalized Expression", x = NULL) +
guides(fill = "none", color = "none") +
scale_y_log10(label = comma) +
theme_niwot() + theme(axis.text.x = element_text(
angle = 45,
vjust = 1,
hjust = 1
))C <- ggplot(data = renamed.exp.long,
aes(x = gene_biotype, y = norm.expression, fill = condition)) +
geom_flat_violin(position = position_nudge(x = 0.2, y = 0),
alpha = 0.8,
trim = FALSE) +
geom_point(
aes(y = norm.expression, color = condition),
position = position_jitter(width = 0.15),
size = 1,
alpha = 0.5
) +
geom_boxplot(width = 0.2,
outlier.shape = NA,
alpha = 0.8) + stat_summary(
fun = mean,
geom = "point",
shape = 23,
size = 2
) +
labs(y = "\nNormalized Expression", x = NULL) +
guides(fill = "none", color = "none") +
scale_y_log10(label = comma) +
theme_niwot() + theme(axis.text.x = element_text(
angle = 45,
vjust = 1,
hjust = 1
))Expression plots
Replicates
AFigure 6A: Normalized expression of genes in undifferentiated and differentiated samples (replicate)
Conditions
BFigure 6B: Normalized expression of genes in undifferentiated and differentiated samples (condition)
Biotypes
CFigure 6C: Normalized expression of genes in undifferentiated and differentiated samples (split based on gene biotype)
Highly expressed small RNAs
renamed.exp.means <-
renamed.exp %>% rowwise() %>% mutate(Diff = mean(c(
Dif_D6_1_S4, Dif_D6_2_S3, Dif_D6_3_S2, Dif_D6_4_S1
)))
renamed.exp.means <-
renamed.exp.means %>% rowwise() %>% mutate(Undiff = mean(c(
Undif_D2_1_S8, Undif_D2_2_S7, Undif_D2_3_S6, Undif_D2_4_S5
)))
df.diff <-
renamed.exp.means %>%
dplyr::select(gene:external_gene_name, Diff) %>%
dplyr::filter(Diff > 0)
df.undiff <-
renamed.exp.means %>%
dplyr::select(gene:external_gene_name, Undiff) %>%
dplyr::filter(Undiff > 0)
df.diff$quart <- ntile(df.diff$Diff, 4)
df.diff$decile <- ntile(df.diff$Diff, 10)
df.undiff$quart <- ntile(df.undiff$Undiff, 4)
df.undiff$decile <- ntile(df.undiff$Undiff, 10)
undiff.75pc <-
df.undiff %>%
dplyr::filter(quart == 4) %>%
dplyr::select(ensembl_gene_id_version)
undiff.90pc <-
df.undiff %>%
dplyr::filter(decile == 10) %>%
dplyr::select(ensembl_gene_id_version)
diff.75pc <-
df.diff %>%
dplyr::filter(quart == 4) %>%
dplyr::select(ensembl_gene_id_version)
diff.90pc <-
df.diff %>%
dplyr::filter(decile == 10) %>%
dplyr::select(ensembl_gene_id_version)
data.A <- diff.90pc %>% column_to_rownames(var="ensembl_gene_id_version")
data.A$diff.90pc = 1
data.B <- undiff.90pc %>% column_to_rownames(var="ensembl_gene_id_version")
data.B$undiff.90pc = 1
decile.data <- merge(data.A, data.B, by=0, all.x=TRUE, all.y=TRUE)
decile.data[is.na(decile.data)] <- 0
decile.data <-
merge(x = decile.data,
y = annot,
by.x = "Row.names",
by.y = "ensembl_gene_id_version",
all.x = TRUE)
decile.data <- decile.data %>% dplyr::select(Row.names:gene_biotype)
decile.data <- decile.data %>% column_to_rownames(var="Row.names")
data.A <- diff.75pc %>% column_to_rownames(var="ensembl_gene_id_version")
data.A$diff.75pc = 1
data.B <- undiff.75pc %>% column_to_rownames(var="ensembl_gene_id_version")
data.B$undiff.75pc = 1
quartile.data <- merge(data.A, data.B, by=0, all.x=TRUE, all.y=TRUE)
quartile.data[is.na(quartile.data)] <- 0
quartile.data <-
merge(x = quartile.data,
y = annot,
by.x = "Row.names",
by.y = "ensembl_gene_id_version",
all.x = TRUE)
quartile.data <- quartile.data %>% dplyr::select(Row.names:gene_biotype)
quartile.data <- quartile.data %>% column_to_rownames(var="Row.names")
inter <- colnames(decile.data)[1:2]
decile.plot <- upset(
decile.data,
inter,
annotations = list(
'gene_biotype' = (
ggplot(mapping = aes(fill = gene_biotype))
+ geom_bar(stat = 'count', position = 'fill')
+ scale_y_continuous(labels = scales::percent_format())
+ scale_fill_manual(
values = c(
'miRNA' = '#8142c8',
'misc_RNA' = '#f62189',
'snRNA' = '#008cd4',
'snoRNA' = '#7c4515',
'scaRNA' = '#e9a3ff',
'sRNA' = '#e7c08b'
)
)
+ ylab('smallRNA proportion')
)
),
queries=list(
upset_query(
intersect=c('diff.90pc', 'undiff.90pc'),
color='orange',
fill='orange',
only_components=c('intersections_matrix', 'Intersection size')
),
upset_query(
intersect='diff.90pc',
color='tomato',
fill='tomato',
only_components=c('intersections_matrix', 'Intersection size')
),
upset_query(
intersect='undiff.90pc',
color='lightseagreen',
fill='lightseagreen',
only_components=c('intersections_matrix', 'Intersection size')
)
),
width_ratio = 0.3,
set_sizes = (
upset_set_size(geom = geom_bar(aes(
fill = gene_biotype, x = group
),
width = 0.8),
position = 'right') + theme(legend.position = "none") +
scale_fill_manual(
values = c(
'miRNA' = '#8142c8',
'misc_RNA' = '#f62189',
'snRNA' = '#008cd4',
'snoRNA' = '#7c4515',
'scaRNA' = '#e9a3ff',
'sRNA' = '#e7c08b'
)
)
),
guides = 'over'
)
inter <- colnames(quartile.data)[1:2]
quartile.plot <- upset(
quartile.data,
inter,
annotations = list(
'gene_biotype' = (
ggplot(mapping = aes(fill = gene_biotype))
+ geom_bar(stat = 'count', position = 'fill')
+ scale_y_continuous(labels = scales::percent_format())
+ scale_fill_manual(
values = c(
'miRNA' = '#8142c8',
'misc_RNA' = '#f62189',
'snRNA' = '#008cd4',
'snoRNA' = '#7c4515',
'scaRNA' = '#e9a3ff',
'sRNA' = '#e7c08b'
)
)
+ ylab('smallRNA proportion')
)
),
queries=list(
upset_query(
intersect=c('diff.75pc', 'undiff.75pc'),
color='orange',
fill='orange',
only_components=c('intersections_matrix', 'Intersection size')
),
upset_query(
intersect='diff.75pc',
color='tomato',
fill='tomato',
only_components=c('intersections_matrix', 'Intersection size')
),
upset_query(
intersect='undiff.75pc',
color='lightseagreen',
fill='lightseagreen',
only_components=c('intersections_matrix', 'Intersection size')
)
),
width_ratio = 0.3,
set_sizes = (
upset_set_size(geom = geom_bar(aes(
fill = gene_biotype, x = group
),
width = 0.8),
position = 'right') + theme(legend.position = "none") +
scale_fill_manual(
values = c(
'miRNA' = '#8142c8',
'misc_RNA' = '#f62189',
'snRNA' = '#008cd4',
'snoRNA' = '#7c4515',
'scaRNA' = '#e9a3ff',
'sRNA' = '#e7c08b'
)
)
),
guides = 'over'
)Intersection plots
quartile
quartile.plotFigure 7A: Gene expression greater than 75th percentile (intersection)
decile
decile.plotFigure 7B: Gene expression greater than 90th percentile (intersection)
Volcano plots
volcanoPlots2 <-
function(res.se,
string,
first,
second,
color1,
color2,
color3,
ChartTitle) {
res.se <- res.se[order(res.se$padj), ]
res.se <-
rownames_to_column(as.data.frame(res.se[order(res.se$padj), ]))
names(res.se)[1] <- "Gene"
res.data <-
merge(res.se,
annot,
by.x = "Gene",
by.y = "ensembl_gene_id_version")
res.data <- res.data %>% mutate_all(na_if, "")
res.data <- res.data %>% mutate_all(na_if, " ")
res.data <-
res.data %>% mutate(gene_symbol = coalesce(external_gene_name, Gene))
fc=1.5
log2fc=log(fc, base = 2)
neg.log2fc = log2fc * -1
res.data$diffexpressed <- "other.genes"
res.data$diffexpressed[res.data$log2FoldChange >= log2fc &
res.data$padj <= 0.05] <-
paste("Higher expression in", first)
res.data$diffexpressed[res.data$log2FoldChange <= neg.log2fc &
res.data$padj <= 0.05] <-
paste("Higher expression in", second)
res.data$delabel <- ""
res.data$delabel[res.data$log2FoldChange >= log2fc
& res.data$padj <= 0.05
&
!is.na(res.data$padj)] <-
res.data$gene_symbol[res.data$log2FoldChange >= log2fc
&
res.data$padj <= 0.05
&
!is.na(res.data$padj)]
res.data$delabel[res.data$log2FoldChange <= neg.log2fc
& res.data$padj <= 0.05
&
!is.na(res.data$padj)] <-
res.data$gene_symbol[res.data$log2FoldChange <= neg.log2fc
&
res.data$padj <= 0.05
&
!is.na(res.data$padj)]
ggplot(res.data,
aes(
x = log2FoldChange,
y = -log10(padj),
col = diffexpressed,
label = delabel
)) +
geom_point(alpha = 0.5) +
xlim(-20, 20) +
theme_classic() +
scale_color_manual(name = "Expression", values = c(color1, color2, color3)) +
# geom_text_repel(
# data = subset(res.data, padj <= 0.05),
# max.overlaps = 15,
# show.legend = F,
# min.segment.length = Inf,
# seed = 42,
# box.padding = 0.5
# ) +
ggtitle(ChartTitle) +
xlab(paste("log2 fold change")) +
ylab("-log10 pvalue (adjusted)") +
theme(legend.text.align = 0)
}g <- volcanoPlots2(
res.UndfvsDiff,
"UndfvsDiff",
"Undf",
"Diff",
"tomato",
"lightseagreen",
"grey",
ChartTitle = "Undifferentiated vs. Differentiated"
)
ggplotly(g)Figure 6: Volcano plot showing genes overexpressed in undifferentiated and differentiated states.
#gHeatmap
Heatmap for the top 30 variable genes:
topVarGenes <- head(order(rowVars(assay(vsd)), decreasing = TRUE), 30)
mat <- assay(vsd)[ topVarGenes, ]
mat <- mat - rowMeans(mat)
mat2 <- merge(mat,
annot,
by.x = 'row.names',
by.y = "ensembl_gene_id_version")
mat2$gene <- paste0(mat2$external_gene_name," (",mat2$gene_biotype,")")
rownames(mat2) <- mat2[,'gene']
mat2 <- mat2[2:9]
heat_colors <- brewer.pal(9, "YlOrRd")
g <- pheatmap(
mat2,
color = heat_colors,
main = "Top 30 variable small RNA genes",
cluster_rows = T,
cluster_cols = T,
show_rownames = T,
border_color = NA,
fontsize = 10,
scale = "row",
fontsize_row = 10
)
gFigure 7: Heat map for top 30 variable small RNA genes
MultiQC report:
MultiQC report is available at this link
Session Information
sessionInfo()
#> R version 4.2.1 (2022-06-23)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 20.04.4 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] ComplexUpset_1.3.3 PupillometryR_0.0.4
#> [3] rlang_1.0.4 reshape2_1.4.4
#> [5] biomaRt_2.52.0 ggrepel_0.9.1
#> [7] genefilter_1.78.0 pheatmap_1.0.12
#> [9] RColorBrewer_1.1-3 DESeq2_1.36.0
#> [11] SummarizedExperiment_1.26.1 Biobase_2.56.0
#> [13] MatrixGenerics_1.8.1 matrixStats_0.62.0
#> [15] GenomicRanges_1.48.0 GenomeInfoDb_1.32.2
#> [17] IRanges_2.30.0 S4Vectors_0.34.0
#> [19] BiocGenerics_0.42.0 plotly_4.10.0
#> [21] forcats_0.5.1 stringr_1.4.0
#> [23] dplyr_1.0.9 purrr_0.3.4
#> [25] readr_2.1.2 tidyr_1.2.0
#> [27] tibble_3.1.8 ggplot2_3.3.6
#> [29] tidyverse_1.3.2 scales_1.2.0
#> [31] knitr_1.39
#>
#> loaded via a namespace (and not attached):
#> [1] googledrive_2.0.0 colorspace_2.0-3 ellipsis_0.3.2
#> [4] XVector_0.36.0 fs_1.5.2 rstudioapi_0.13
#> [7] farver_2.1.1 bit64_4.0.5 AnnotationDbi_1.58.0
#> [10] fansi_1.0.3 lubridate_1.8.0 xml2_1.3.3
#> [13] codetools_0.2-18 splines_4.2.1 cachem_1.0.6
#> [16] geneplotter_1.74.0 jsonlite_1.8.0 broom_1.0.0
#> [19] annotate_1.74.0 dbplyr_2.2.1 png_0.1-7
#> [22] compiler_4.2.1 httr_1.4.3 backports_1.4.1
#> [25] assertthat_0.2.1 Matrix_1.4-1 fastmap_1.1.0
#> [28] lazyeval_0.2.2 gargle_1.2.0 cli_3.3.0
#> [31] prettyunits_1.1.1 htmltools_0.5.3 tools_4.2.1
#> [34] gtable_0.3.0 glue_1.6.2 GenomeInfoDbData_1.2.8
#> [37] rappdirs_0.3.3 Rcpp_1.0.9 cellranger_1.1.0
#> [40] jquerylib_0.1.4 vctrs_0.4.1 Biostrings_2.64.0
#> [43] crosstalk_1.2.0 xfun_0.31 rvest_1.0.2
#> [46] lifecycle_1.0.1 XML_3.99-0.10 googlesheets4_1.0.0
#> [49] zlibbioc_1.42.0 vroom_1.5.7 hms_1.1.1
#> [52] parallel_4.2.1 curl_4.3.2 yaml_2.3.5
#> [55] memoise_2.0.1 sass_0.4.2 stringi_1.7.8
#> [58] RSQLite_2.2.15 highr_0.9 filelock_1.0.2
#> [61] BiocParallel_1.30.3 pkgconfig_2.0.3 bitops_1.0-7
#> [64] evaluate_0.15 lattice_0.20-45 patchwork_1.1.1
#> [67] htmlwidgets_1.5.4 labeling_0.4.2 bit_4.0.4
#> [70] tidyselect_1.1.2 plyr_1.8.7 magrittr_2.0.3
#> [73] bookdown_0.27 R6_2.5.1 generics_0.1.3
#> [76] DelayedArray_0.22.0 DBI_1.1.3 pillar_1.8.0
#> [79] haven_2.5.0 withr_2.5.0 survival_3.3-1
#> [82] KEGGREST_1.36.3 RCurl_1.98-1.8 modelr_0.1.8
#> [85] crayon_1.5.1 utf8_1.2.2 BiocFileCache_2.4.0
#> [88] tzdb_0.3.0 rmarkdown_2.14 progress_1.2.2
#> [91] locfit_1.5-9.6 grid_4.2.1 readxl_1.4.0
#> [94] data.table_1.14.2 blob_1.2.3 rmdformats_1.0.4
#> [97] reprex_2.0.1 digest_0.6.29 xtable_1.8-4
#> [100] munsell_0.5.0 viridisLite_0.4.0 bslib_0.4.0